Cite this Article

Sorayya Mirzapour, Fateme Sadat Mousavi, Hamid Reza Ahmadifar, 2025. "Bio-AI Hybrids Using Biological Neural Patterns to Train Artificial Models", International Journal of Research in Artificial Intelligence and Data Science(IJRAIDS)1(1): 73-87.

The International Journal of Research in Artificial Intelligence and Data Science (IJRAIDS)
© 2025 by IJRAIDS
Volume 1 Issue 1
Year of Publication : 2025
Authors : Author Name
Doi : XXXX XXXX XXXX

Keywords

Brain-inspired AI, spike trains, EEG signals, connectomics, neuromorphic computing, cognitive modeling, and bio-AI hybrids are all examples of these.

Abstract

This study looks at Bio-AI Hybrids, which are AI models that integrate patterns from biological brains to make learning easier for computers. Bio-AI Hybrids learn in a different way than previous AI systems that are based on the brain. They employ genuine neural data such spike trains, EEG recordings, functional imaging, and brain connectomes. This strategy based on biology makes AI models work better, be more adaptable, and be able to generalize, even when there isn't much data or there is a lot of noise.

We look at many ways to add biological signals to artificial networks, such as spike-timing-dependent learning, biologically informed network topologies, and brain-inspired regularization methods. Vision, reinforcement learning, and cognitive modeling case studies show that learning speed, robustness, and interpretability have all improved a lot.

The paper talks about more than just technical advancements. It also talks about moral and philosophical difficulties, especially when it comes to data consent, neuroprivacy, and the idea of computer systems that act like brains. We discuss about issues including data variability, processing in real time, and the need for specialized neuromorphic hardware to get the most out of these systems.

Introduction

The human brain is the most advanced type of biological computing because it can accomplish many different mental tasks faster, more flexibly, and more generally than any other type of brain. The fact that it can learn from minimal data, reason based on the situation, and adapt to new environments makes it a constant source of inspiration for the field of artificial intelligence (AI). In the past ten years, artificial neural networks (ANNs) have come a long way. This has led to improvements in image recognition, natural language processing, and reinforcement learning. But they still don't have biological intelligence in several fundamental ways. Some of these are being able to learn new things without forgetting everything, being able to learn from noise, and being able to learn new things without losing everything. Deep learning models often need millions of labeled examples and a lot of computing power, while the brain does similar things with less energy and less supervision. This disparity has caused researchers to look more closely at biological neural networks, not just as abstract concepts, but as real sources of information that may be utilized to improve machine intelligence. It has become easier to collect and study large neural datasets thanks to the growth of fields that combine many areas of study, such as computational neuroscience, neuroinformatics, and brain-machine interface. These datasets include anything from high-resolution spike trains and local field potentials to large-scale functional imaging and structural connectomics.